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Journal articles on the topic 'Device Classification'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

S, Roopalakshmi. "Automated Medical Device Classification." International Journal of Innovative Research in Information Security 11, no. 02 (2025): 106–9. https://doi.org/10.26562/ijiris.2025.v1102.07.

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Manual categorization of medical devices according to regulatory guidelines such as CDSCO (Central Drugs Standard Control Organization) is generally time-consuming, variable, and susceptible to human error. This article describes an automated system of classification based on Microsoft's MiniLM-L12-H384-uncased model, fine-tuned using a dataset of device descriptions, classes, and respective departments. The model is enhanced further by using TF-IDF vectorization and cosine similarity for the determination of the nearest matching device. Experimental evidence shows better classification accura

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Henschke, Cornelia, Dimitra Panteli, Matthias Perleth, and Reinhard Busse. "TAXONOMY OF MEDICAL DEVICES IN THE LOGIC OF HEALTH TECHNOLOGY ASSESSMENT." International Journal of Technology Assessment in Health Care 31, no. 5 (2015): 324–30. http://dx.doi.org/10.1017/s0266462315000562.

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Objectives: The suitability of general HTA methodology for medical devices is gaining interest as a topic of scientific discourse. Given the broad range of medical devices, there might be differences between groups of devices that impact both the necessity and the methods of their assessment. Our aim is to develop a taxonomy that provides researchers and policy makers with an orientation tool on how to approach the assessment of different types of medical devices.Methods: Several classifications for medical devices based on varying rationales for different regulatory and reporting purposes wer

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Munzner, R. "U.S. medical device classification." IEEE Engineering in Medicine and Biology Magazine 23, no. 1 (2004): 207–8. http://dx.doi.org/10.1109/memb.2004.1297204.

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Mhetre, Nalini A., Arvind V. Deshpande, and Parikshit Narendra Mahalle. "Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 135–42. http://dx.doi.org/10.35940/ijeat.e2688.0610521.

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Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is proactive device classification with the logically semantic type and using

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Nalini, A. Mhetre, V. Deshpande Arvind, and Narendra Mahalle Parikshit. "Device Classification-Based Context Management for Ubiquitous Computing using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 5 (2021): 135–42. https://doi.org/10.35940/ijeat.E2688.0610521.

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Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is proactive device classification with the logically semantic type and using

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Peter, Lukas, Ladislav Hajek, Petra Maresova, Martin Augustynek, and Marek Penhaker. "Medical Devices: Regulation, Risk Classification, and Open Innovation." Journal of Open Innovation: Technology, Market, and Complexity 6, no. 2 (2020): 42. http://dx.doi.org/10.3390/joitmc6020042.

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In the Czech Republic, the medical device industry is an important sector with a strong tradition and has high added value and perspectives in demand under changing demographic and social structures. The aim of this article is to describe and analyze the complex issues of the new European Commission Medical Device Regulation (MDR) 2017/745 from the perspective of the strategic decisions of companies that have to comply with the requirements imposed on them by the new legislation and at the same time fulfill their own business needs and goals. The legislative changes significantly affect the st

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SILVER, ERIC, WILLIAM R. SMITH, and STEVEN BANKS. "Constructing Actuarial Devices for Predicting Recidivism." Criminal Justice and Behavior 27, no. 6 (2000): 733–64. http://dx.doi.org/10.1177/0093854800027006004.

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A recent contribution to the prediction literature by Steadman et al. features a novel “iterative classification” procedure for constructing risk screening devices. In this article, the authors apply the iterative classification procedure to a large recidivism data set, across a range of recidivism outcomes and cross-validation conditions. The purpose of this study is to assess the generalizability of the iterative classification procedure and to draw comparisons with more traditional methods of device construction. Results show the iterative classification procedure to outperform other standa

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Vranić, Edina. "Classification and evaluation of medical devices." Bosnian Journal of Basic Medical Sciences 3, no. 2 (2003): 42–45. http://dx.doi.org/10.17305/bjbms.2003.3554.

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Medical devices and medical disposables contribute significantly to the quality and effectiveness of the health care system. It is necessary to commit scientifically sound regulatory environment that will provide consumers with the best medical care. This includes continued services to small manufacturers, readily available guidance on FDA requirements, predictable and reasonable response times on applications for marketing, and equitable enforcement. But in the public interest, this commitment to the industry must be coupled with a reciprocal commitment: that medical device firms will meet hi

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Kodati, Sarangam, Kumbala Pradeep Reddy, Thotakura Veerananna, S. Govinda Rao, and G. Anil Kumar. "Security Framework Connection Assistance for IoT Device Secure Data communication." E3S Web of Conferences 309 (2021): 01061. http://dx.doi.org/10.1051/e3sconf/202130901061.

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Today, Internet of Things (IoT) services has been increasing extensively because of their optimum device sizes and their developed network infrastructure that includes devices based on internet embedded with various sensors, actuators, communication, and storage components providing connection and data exchange. Presently number of industries use vast number of IoT devices, there are some challenges like reducing the risks and threats that exposure, accommodating the huge number of IoT devices in network and providing secure vulnerabilities have risen. Supervised learning has recently been gai

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Zhang, Yi, Paolo Masci, Paul Jones, and Harold Thimbleby. "Research: User Interface Software Errors in Medical Devices: Study of U.S. Recall Data." Biomedical Instrumentation & Technology 53, no. 3 (2019): 182–94. http://dx.doi.org/10.2345/0899-8205-53.3.182.

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AbstractThe current work assessed U.S. medical device recalls during 2012–15, with the goal of under-standing the impact and nature of user interface (UI) software errors in medical devices. Based on information from the Food and Drug Administration's public and internal recall databases, 423 (~140/year) medical device recalls were identified as resulting from UI software errors, which accounted for nearly one-half of recalls caused by software errors during the same period. A total of 499 UI software errors were identified as the root causes of medical device recalls, and a detailed classific

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Afifah, Nurul, and Deris Stiawan. "The Implementation of Deep Neural Networks Algorithm for Malware Classification." Computer Engineering and Applications Journal 8, no. 3 (2019): 189–202. http://dx.doi.org/10.18495/comengapp.v8i3.294.

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Malware is very dangerous while attacked a device system. The device that can be attacked by malware is a Mobile Phone such an Android. Antivirus in the Android device is able to detect malware that has existed but antivirus has not been able to detect new malware that attacks an Android device. In this issue, malware detection techniques are needed that can grouping the files between malware or non-malware (benign) to improve the security system of Android devices. Deep Learning is the proposed method for solving problems in malware detection techniques. Deep Learning algorithm such as Deep N

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Zou, Qingsong, Qing Li, Ruoyu Li, et al. "IoTBeholder." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (2022): 1–26. http://dx.doi.org/10.1145/3580890.

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With the deployment of a growing number of smart home IoT devices, privacy leakage has become a growing concern. Prior work on privacy-invasive device localization, classification, and activity identification have proven the existence of various privacy leakage risks in smart home environments. However, they only demonstrate limited threats in real world due to many impractical assumptions, such as having privileged access to the user's home network. In this paper, we identify a new end-to-end attack surface using IoTBeholder, a system that performs device localization, classification, and use

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Chong, Ming Ki, and Hans Gellersen. "Usability classification for spontaneous device association." Personal and Ubiquitous Computing 16, no. 1 (2011): 77–89. http://dx.doi.org/10.1007/s00779-011-0421-1.

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Blumenthal, P., and A. Raatz. "Classification of electrocaloric cooling device types." EPL (Europhysics Letters) 115, no. 1 (2016): 17004. http://dx.doi.org/10.1209/0295-5075/115/17004.

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Chun-qiao Wang, Run-tao Chen, Ying-zhuo Zhang, Jun-jun Tao, and Yi-ting Zhu. "Multifunctional gas safety monitoring and classification warning device." Global Journal of Engineering and Technology Advances 19, no. 2 (2024): 081–88. http://dx.doi.org/10.30574/gjeta.2024.19.2.0081.

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Due to the absence of automatic flameout and other essential functionalities in these devices, incidents such as gas leaks can easily lead to explosions. As a result, an autonomous safety monitoring and early warning device has been specifically developed for traditional gas stove equipment. This device is capable of activating a multi-tiered alarm and control mechanism when it reaches a predetermined threshold concentration. The main control of the Arduino Uno R3 microcontroller development board serves as the foundation for this device, which utilizes the MQ-5 gas sensor and 1602 liquid crys

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Chun-qiao, Wang, Chen Run-tao, Zhang Ying-zhuo, Tao Jun-jun, and Zhu Yi-ting. "Multifunctional gas safety monitoring and classification warning device." Global Journal of Engineering and Technology Advances 19, no. 2 (2024): 081–88. https://doi.org/10.5281/zenodo.13691700.

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Due to the absence of automatic flameout and other essential functionalities in these devices, incidents such as gas leaks can easily lead to explosions. As a result, an autonomous safety monitoring and early warning device has been specifically developed for traditional gas stove equipment. This device is capable of activating a multi-tiered alarm and control mechanism when it reaches a predetermined threshold concentration. The main control of the Arduino Uno R3 microcontroller development board serves as the foundation for this device, which utilizes the MQ-5 gas sensor and 1602 liquid crys

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Piat, Sebastien, Nairi Usher, Simone Severini, Mark Herbster, Tommaso Mansi, and Peter Mountney. "Image classification with quantum pre-training and auto-encoders." International Journal of Quantum Information 16, no. 08 (2018): 1840009. http://dx.doi.org/10.1142/s0219749918400099.

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Computer vision has a wide range of applications from medical image analysis to robotics. Over the past few years, the field has been transformed by machine learning and stands to benefit from potential advances in quantum computing. The main challenge for processing images on current and near-term quantum devices is the size of the data such devices can process. Images can be large, multidimensional and have multiple color channels. Current machine learning approaches to computer vision that exploit quantum resources require a significant amount of manual pre-processing of the images in order

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Franks, Zanna, and Hannah G Krause. "Pessary management for pelvic organ prolapse: a review of clinical practice and Australian medical device regulations." Australian and New Zealand Continence Journal 29, no. 3 (2025): 67–73. https://doi.org/10.1071/cj23013.

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Pelvic organ prolapse affects up to 50% of women throughout life. Management can be conservative or surgical. Pessaries have become an integral component to management of pelvic organ prolapse, providing symptom improvement and enhancing quality of life. After careful clinical assessment, women choosing pessary management are offered either self-care or clinician-based pessary care. Pessary care can be offered by a range of clinicians, including doctors, nurses and physiotherapists. In this article we review the literature on the historical use of pessaries and how they have changed to be the

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Lokhande, Meghana P., Dipti Durgesh Patil, Lalit V. Patil, and Mohammad Shabaz. "Machine-to-Machine Communication for Device Identification and Classification in Secure Telerobotics Surgery." Security and Communication Networks 2021 (August 27, 2021): 1–16. http://dx.doi.org/10.1155/2021/5287514.

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The capacity of machine objects to communicate autonomously is seen as the future of the Internet of Things (IoT), but machine-to-machine communication (M2M) is also gaining traction. In everyday life, security, transportation, industry, and healthcare all employ this paradigm. Smart devices have the ability to detect, handle, store, and analyze data, resulting in major network issues such as security and reliability. There are numerous vulnerabilities linked with IoT devices, according to security experts. Prior to performing any activities, it is necessary to identify and classify the device

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Воскобойников, Михаил Леонтьевич, Роман Константинович Федоров, and Геннадий Михайлович Ружников. "Automatic definition of web service call zones based on the classification of context of the mobile device." Вычислительные технологии, no. 2 (June 22, 2021): 88–97. http://dx.doi.org/10.25743/ict.2021.26.2.007.

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Предложен метод автоматизации активации устройств Интернета вещей на основе классификации геопозиции мобильного устройства. В отличие от других методов пользователь обучает систему активации устройств с помощью примеров и контрпримеров, что значительно снижает требования к квалификации пользователя. Проведено тестирование метода на таких двух устройствах, как шлагбаум и электромеханический замок двери. Полученные результаты тестирования позволяют судить о работоспособности метода и возможности его использования в системах умного дома и города. Most IoT devices provide an application programmin

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Riadi, Imam, Abdul Fadlil, and Basit Adhi Prabowo. "Optimalisasi Layanan Keamanan Captive Portal Menggunakan Klasifikasi Logistic Regression." INFORMAL: Informatics Journal 9, no. 3 (2024): 189. https://doi.org/10.19184/isj.v9i3.44189.

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Privacy has become a major concern with the rapid adoption of various smart devices and internet connections. The randomized MAC (Media Access Control) address for each device was implemented for privacy. Problems arose when implementing randomized MAC addresses on captive portals with connection limitations per user. Random classification by VOUI of the device used to assist the device elimination decisions in the captive portal. MAC address data was obtained from devices connected to the captive portal. The data is processed to be grouped into two separate classes, whether random or not, wit

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You, Shingchern D. "Classification of Relaxation and Concentration Mental States with EEG." Information 12, no. 5 (2021): 187. http://dx.doi.org/10.3390/info12050187.

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In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency repr

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Keutzer, Lina, and Ulrika SH Simonsson. "Medical Device Apps: An Introduction to Regulatory Affairs for Developers." JMIR mHealth and uHealth 8, no. 6 (2020): e17567. http://dx.doi.org/10.2196/17567.

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The Poly Implant Prothèse (PIP) scandal in France prompted a revision of the regulations regarding the marketing of medical devices. The new Medical Device Regulation (MDR [EU]) 2017/745 was developed and entered into force on May 25, 2017. After a transition period of 3 years, the regulations must be implemented in all EU and European Economic Area member states. The implementation of this regulation bears many changes for medical device development and marketing, including medical device software and mobile apps. Medical device development and marketing is a complex process by which manufact

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Semendawai, Jaka Naufal, Deris Stiawan, and Iwan Pahendra. "Shellcode Classification with Machine Learning Based on Binary Classification." Jurnal Indonesia Sosial Teknologi 6, no. 2 (2025): 833–44. https://doi.org/10.59141/jist.v6i2.3233.

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The Internet can link one person to another using their respective devices. The internet itself has both positive and negative impacts. One example of the internet's negative impact is malware that can disrupt or even kill a device or its users; that is why cyber security is required. Many methods can be used to prevent or detect malware. One of the efforts is to use machine learning techniques. The training and testing dataset for the experiments is derived from the UNSW_NB15 dataset. K-Nearest Neighbour (KNN), Decision Tree, and Naïve Bayes classifiers are implemented to classify whether a r

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Osipov, Ilya V. "Cubios Transreality Puzzle as a Mixed Reality Object." International Journal of Virtual and Augmented Reality 1, no. 2 (2017): 1–17. http://dx.doi.org/10.4018/ijvar.2017070101.

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The author proposes to establish a separate class of electronic mechanical puzzles being the object of “mixed reality”, presents a self-engineered example of such a device, and reviews similar devices produced by other developers. Close relationships of such devices with tangible user interfaces are described. A Cubios device is presented as an illustration of a mixed reality puzzle along with its variants developed by the author. The purpose of this paper is to present a new mixed reality device, review similar devices, propose the classification of such devices, identify their relationships

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McTighe, Dr. HS (hc), Timothy, John Keggi, MD, David Stulberg, MD, Louis Keppler, MD, Declan Brazil, PhD, and Edward McPherson, MD. "Total Hip Stem Classification System." Reconstructive Review 4, no. 2 (2014): 24–28. http://dx.doi.org/10.15438/rr.v4i2.70.

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The aim of this paper is to review the influx of short stems for total hip arthroplasty. Not all short stems are created equal concerning fixation points for implant stability and length of engagement of the device in the proximal femur. Some devices are stabilized in the head, neck, metaphysis and metaphysis/diaphysis. Depending on stabilization and engagement area different short stems can have different indications, contraindications and clinical outcomes. As a result of our findings JISRF developed a classification system based on implant stabilization point and overall stem length.Keyword

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Uss, A. Y., and A. V. Chernyshev. "Classification of Vortex Jet Devices for Gas Flow Control in Pneumo-Hydraulic Systems." Proceedings of Higher Educational Institutions. Маchine Building, no. 07 (724) (July 2020): 43–58. http://dx.doi.org/10.18698/0536-1044-2020-7-43-58.

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This article presents a review and analysis of the literature on the development and study of vortex jet devices that are used as control valves employing the principle of the vortex flow of the working medium. The article discusses vortex valves without mechanically moving parts, as well as semi-mechanical vortex valves. The principle of operation of a vortex jet device is described and, for the first time, a classification of vortex jet devices by a number of structural and functional features is carried out. The analysis of technical solutions identifies the advantages and disadvantages of

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Jaleel, Ahmed Etihad, and Hesham Adnan Alabbasi. "Online hand position detection and classification system using multiple classification algorithms." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 346. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp346-357.

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Hand position recognition is very significant for human-computer interaction. Different kinds of devices and technologies can be used for data acquisition; each has its specification and accuracy, one of these devices is Kinect V2 sensor. A three-dimensional location of the skeleton joints is taken from the Kinect device to create three types of data, the first is joint position raw data, the second is angles between joints, the third is combined of both types. These three types of data are used to train four classifiers, which are support vector machines, random forest, k nearest neighbors, a

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Jaleel, Ahmed Etihad, and Hesham Adnan Alabbasi. "Online hand position detection and classification system using multiple classification algorithms." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 346–57. https://doi.org/10.11591/ijeecs.v28.i1.pp346-357.

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Hand position recognition is very significant for human-computer interaction. Different kinds of devices and technologies can be used for data acquisition; each has its specification and accuracy, one of these devices is Kinect V2 sensor. A three-dimensional location of the skeleton joints is taken from the Kinect device to create three types of data, the first is joint position raw data, the second is angles between joints, the third is combined of both types. These three types of data are used to train four classifiers, which are support vector machines, random forest, k nearest neighbors, a

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Kim, Jiyeon, Minsun Shim, Seungah Hong, Yulim Shin, and Eunjung Choi. "Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning." Applied Sciences 10, no. 19 (2020): 7009. http://dx.doi.org/10.3390/app10197009.

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As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security mo

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Załuska, Roman, Anna Justyna Milewska, Joanna Olszewska, and Wojciech Drygas. "Analysis of Indications for Electrotherapy Using Classification Trees." Studies in Logic, Grammar and Rhetoric 66, no. 3 (2021): 587–96. http://dx.doi.org/10.2478/slgr-2021-0034.

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Abstract Electrotherapy is a dynamically developing method of treatment of sinus node dysfunction and atrioventricular conduction disturbances. It is an extremely important method used in the treatment of heart failure. The aim of this paper was to use classification trees for the differentiation between patients implanted with one of the three electrotherapy devices, i.e. SC-VVI/AAI, DC-DDD, ICD/CRT. The analysed data concerned 2071 patients who underwent implantation or device replacement procedures in the years 2010–2018, hospitalized in a coronary care unit. CART-type classification trees

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Hanschett, Marilyn, and Julie Naunheim-Hipps. "A Call for Easier Safety Device Classification." Journal of Vascular Access Devices 7, no. 1 (2002): 40–41. http://dx.doi.org/10.2309/108300802775843112.

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Rasras, Rashad J. "Neurocomputing Device for Image Processing and Classification." Journal of Computer Science, no. 2 (February 1, 2005): 89–91. http://dx.doi.org/10.3844/jcssp.2005.89.91.

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Kelly, Linda J., and Tracey Jones. "Medical device classification: focus on vascular access." British Journal of Nursing 27, no. 14 (2018): S14—S19. http://dx.doi.org/10.12968/bjon.2018.27.14.s14.

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Kuhn, Roland. "Dialogue device for call screening and classification." Journal of the Acoustical Society of America 116, no. 6 (2004): 3257. http://dx.doi.org/10.1121/1.1852988.

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Alawiya, Tuti, Muhammad Ridho Isdi, Meqorry Yusfi, and Harmadi Harmadi. "Fabric materials classification device using YOLOv8 algorithm." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 3 (2025): 1479. https://doi.org/10.11591/ijeecs.v38.i3.pp1479-1488.

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The fashion industry in Indonesia significantly contributes to the country’s creative economy. However, public knowledge about various types of fabric materials is still limited, often leading to fraud. This research aims to develop a device that can classify fabric materials based on their structure using computer vision techniques. The device uses a digital microscope endoscope magnifier 1600x USB camera to capture fabric structure images and the YOLOv8 algorithm to classify 17 types of fabric materials from 1,700 raw image data. The research methodology includes collecting fabric image data

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Zhang, Zhen, Yibing Li, Chao Wang, Meiyu Wang, Ya Tu, and Jin Wang. "An Ensemble Learning Method for Wireless Multimedia Device Identification." Security and Communication Networks 2018 (October 15, 2018): 1–9. http://dx.doi.org/10.1155/2018/5264526.

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In the last decade, wireless multimedia device is widely used in many fields, which leads to efficiency improvement, reliability, security, and economic benefits in our daily life. However, with the rapid development of new technologies, the wireless multimedia data transmission security is confronted with a series of new threats and challenges. In physical layer, Radio Frequency Fingerprinting (RFF) is a unique characteristic of IoT devices themselves, which can difficultly be tampered. The wireless multimedia device identification via Radio Frequency Fingerprinting (RFF) extracted from radio

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Kocabas, S. "A review of learning." Knowledge Engineering Review 6, no. 3 (1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.

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AbstractLearning is one of the important research fields in artificial intelligence. This paper begins with an outline of the definitions of learning and intelligence, followed by a discussion of the aims of machine learning as an emerging science, and an historical outline of machine learning. The paper then examines the elements and various classifications of learning, and then introduces a new classification of learning based on the levels of representation and learning as knowledge-, symboland device-level learning. Similarity- and explanation-based generalization and conceptual clustering

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Sikoparija, Branko, Predrag Matavulj, Isidora Simovic, et al. "Classification accuracy and compatibility across devices of a new Rapid-E+ flow cytometer." Atmospheric Measurement Techniques 17, no. 17 (2024): 5051–70. http://dx.doi.org/10.5194/amt-17-5051-2024.

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Abstract. The study evaluated a new model of a Plair SA airflow cytometer, Rapid-E+, and assessed its suitability for airborne pollen monitoring within operational networks. Key features of the new model are compared with the previous one, Rapid-E. A machine learning algorithm is constructed and evaluated for (i) classification of reference pollen types in laboratory conditions and (ii) monitoring in real-life field campaigns. The second goal of the study was to evaluate the device usability in forthcoming monitoring networks, which would require similarity and reproducibility of the measureme

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T. Pravalika, Yakub Pasha, M. Vinay Kumar, and Pendli Venu. "A HYBRID DEEP ENSEMBLE FRAMEWORK FOR INTELLIGENT FAULT DETECTION AND ANOMALY DIAGNOSIS IN MEDICAL IOT SYSTEMS." Scientific Digest : Journal of Applied Engineering 13, no. 7(1) (2025): 240–46. https://doi.org/10.70864/joae.2025.v13.i7(1).pp240-246.

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The widespread adoption of Internet of Things (IoT) devices in healthcare has underscored the need for intelligent systems to accurately classify device operational states. With over 60% of hospitals utilizing IoT for patient monitoring and the global medical IoT market projected to reach USD 254.2 billion by 2026, ensuring device reliability is critical. However, studies indicate that approximately 15% of medical IoT devices suffer from undetected malfunctions due to inefficient classification systems. Traditional manual monitoring methods are prone to errors, cannot handle large-scale real-t

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Mayrhofer, René, and Stephan Sigg. "Adversary Models for Mobile Device Authentication." ACM Computing Surveys 54, no. 9 (2022): 1–35. http://dx.doi.org/10.1145/3477601.

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Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of

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Bezerra, Vitor Hugo, Victor Guilherme Turrisi da Costa, Sylvio Barbon Junior, Rodrigo Sanches Miani, and Bruno Bogaz Zarpelão. "IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices." Sensors 19, no. 14 (2019): 3188. http://dx.doi.org/10.3390/s19143188.

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Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architect

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Kwon, Dongseok, Hyeongsu Kim, Kyu-Ho Lee, et al. "Super-steep synapses based on positive feedback devices for reliable binary neural networks." Applied Physics Letters 122, no. 10 (2023): 102101. http://dx.doi.org/10.1063/5.0131235.

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This work proposes positive feedback (PF) device-based synaptic devices for reliable binary neural networks (BNNs). Due to PF operation, the fabricated PF device shows a high on/off current ratio (2.69 [Formula: see text] 107). The PF device has a charge-trap layer by which the turn-on voltage ( Von) of the device can be adjusted by program/erase operations and a long-term memory function is implemented. Also, due to the steep switching characteristics of the PF device, the conductance becomes tolerant to the retention time and the variation in turn-on voltage. Simulations show that high accur

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Ismoilov, Mamurjon. "ZIGBEE WIRELESS TECHNOLOGY: OVERVIEW OF APPLICATIONS, TOPOLOGIES AND CLASSIFICATION STANDARDS." Research and implementation 2, no. 2 (2024): 238–44. https://doi.org/10.5281/zenodo.10701920.

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ZigBee has become a popular device networking technology. ZigBee is a wireless communication technology widely used in Internet of Things devices. This article discusses the features of ZigBee and its application in IoT. Theoretical studies of the proposed options were carried out and the author's conclusions were given

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Hou, Xiangfeng, and Bo Qi. "Basketball Training Posture Monitoring Based on Intelligent Wearable Device." Mobile Information Systems 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/4945534.

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Aiming at the problems of low monitoring accuracy, long time, and poor effect in the current basketball training posture monitoring method, a basketball training posture monitoring method based on intelligent wearable devices is proposed. By analyzing the concept and classification of intelligent wearable devices, the attitude monitoring technology based on intelligent wearable devices is studied. A two-stage Kalman filter is used to correct the error caused by the drift of the gyroscope signal in the intelligent wearable device by constructing an adaptive acceleration error covariance matrix.

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Guo, Zhonghao. "Synaptic device-based neuromorphic computing in artificial intelligence." Applied and Computational Engineering 65, no. 1 (2024): 253–59. http://dx.doi.org/10.54254/2755-2721/65/20240511.

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The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic

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Hwang, Yeongjin, Jeong Hoon Jeon, Juhyun Lee, Jonghyuk Yoon, Felix Sunjoo Kim, and Hyungjin Kim. "Effect of Threshold Voltage Window and Variation of Organic Synaptic Transistor for Neuromorphic System." Journal of Nanoscience and Nanotechnology 21, no. 8 (2021): 4303–9. http://dx.doi.org/10.1166/jnn.2021.19393.

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Synaptic devices, which are considered as one of the most important components of neuromorphic system, require a memory effect to store weight values, a high integrity for compact system, and a wide window to guarantee an accurate programming between each weight level. In this regard, memristive devices such as resistive random access memory (RRAM) and phase change memory (PCM) have been intensely studied; however, these devices have quite high current-level despite their state, which would be an issue if a deep and massive neural network is implemented with these devices since a large amount

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Gong, Eun Jeong, Chang Seok Bang, and Jae Jun Lee. "Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study." Biomimetics 9, no. 12 (2024): 783. https://doi.org/10.3390/biomimetics9120783.

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Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, a deep learning endoscopic image classification model was created to automatically categorize all phases of gastric carcinogenesis using an edge computing device. Design: A total of 15,910 endoscopic images were collected retrospectively and randomly assigned to trai

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Shcheglovitova, Elena, and Anastasia Zotova. "CLASSIFICATION OF TRANSFORMER SUBSTATIONS." Energy Systems 9, no. 1 (2024): 122–27. http://dx.doi.org/10.34031/es.2024.1.016.

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The transfer of electrical energy from the power station to the consumer over long distances is possible by changing the voltage level. An integral part in the operation of electrical networks is an electromagnetic device that changes the voltage level - a power transformer. The most important is voltage transformation, first in order to increase the voltage coming from the generator buses of a power plant to reduce power losses during long-distance transmission, and then lower it to the level required by the consumer's power receiving devices. The article presents the variety of transformer s

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Dang, Fangfang, Lijing Yan, Ying Yang, Shuai Li, Dingding Li, and Dong Niu. "Research on machine learning-based device fingerprint recognition technology for power internet of things." Journal of Physics: Conference Series 2781, no. 1 (2024): 012024. http://dx.doi.org/10.1088/1742-6596/2781/1/012024.

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Abstract With the rapid development of Electric Power Internet of Things (IoT) technology, a large number of devices are being networked and exposed to cyberspace. Due to the disparity in security design levels and lax management during usage, electric power terminals are susceptible targets for network attackers. This not only causes losses to device owners but also poses a threat to the overall cybersecurity of the network, as compromised devices can serve as nodes for botnets. The importance of addressing this issue cannot be underestimated. Asset identification is a prerequisite for the se

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